Misc

codes/data_scripts/extract_subimages.py

@@ -20,8 +20,8 @@ def main():
     # CV_IMWRITE_PNG_COMPRESSION from 0 to 9. A higher value means a smaller size and longer
     # compression time. If read raw images during training, use 0 for faster IO speed.
     if mode == 'single':
-        opt['input_folder'] = 'F:\\4k6k\\datasets\\imagesets\\new'
-        opt['save_folder'] = 'F:\\4k6k\\datasets\\imagesets\\unfiltered_tiled_2x'
+        opt['input_folder'] = 'F:\\4k6k\\datasets\\hands_on_hc\\images'
+        opt['save_folder'] = 'F:\\4k6k\\datasets\\imagesets\\tiled_512px'
         opt['crop_sz'] = 512  # the size of each sub-image
         opt['step'] = 440  # step of the sliding crop window
         opt['thres_sz'] = 120  # size threshold
@@ -82,9 +82,6 @@ def extract_single(opt, split_img=False):
     if not osp.exists(save_folder):
         os.makedirs(save_folder)
         print('mkdir [{:s}] ...'.format(save_folder))
-    else:
-        print('Folder [{:s}] already exists. Exit...'.format(save_folder))
-        sys.exit(1)
     img_list = data_util._get_paths_from_images(input_folder)
 
     def update(arg):

codes/distill_torchscript.py

@@ -15,17 +15,70 @@ if __name__ == "__main__":
     netG = define_G(opt)
     dummyInput = torch.rand(1,3,8,8)
 
-    torchscript = False
-    if torchscript:
+    mode = 'torchscript'
+    if mode == 'torchscript':
         print("Tracing generator network..")
         traced_netG = torch.jit.trace(netG, dummyInput)
         traced_netG.save('../results/ts_generator.zip')
-        print(traced_netG)
-    else:
+
+        print(traced_netG.code)
+        for i, module in enumerate(traced_netG.RRDB_trunk.modules()):
+            print(i, str(module))
+    elif mode == 'onnx':
         print("Performing onnx trace")
         input_names = ["lr_input"]
         output_names = ["hr_image"]
         dynamic_axes = {'lr_input': {0: 'batch', 1: 'filters', 2: 'h', 3: 'w'}, 'hr_image': {0: 'batch', 1: 'filters', 2: 'h', 3: 'w'}}
 
         torch.onnx.export(netG, dummyInput, "../results/gen.onnx", verbose=True, input_names=input_names,
-                          output_names=output_names, dynamic_axes=dynamic_axes, opset_version=11)
+                          output_names=output_names, dynamic_axes=dynamic_axes, opset_version=11)
+    elif mode == 'trace':
+        out = netG.forward(dummyInput)[0]
+        print(out.shape)
+        # Build the graph backwards.
+        graph = build_graph(out, 'output')
+
+def get_unique_id_for_fn(fn):
+    return (str(fn).split(" object at ")[1])[:-1]
+
+class GraphNode:
+    def __init__(self, fn):
+        self.name = (str(fn).split(" object at ")[0])[1:]
+        self.fn = fn
+        self.children = {}
+        self.parents = {}
+
+    def add_parent(self, parent):
+        self.parents[get_unique_id_for_fn(parent)] = parent
+
+    def add_child(self, child):
+        self.children[get_unique_id_for_fn(child)] = child
+
+class TorchGraph:
+    def __init__(self):
+        self.tensor_map = {}
+
+    def get_node_for_tensor(self, t):
+        return self.tensor_map[get_unique_id_for_fn(t)]
+
+    def init(self, output_tensor):
+        self.build_graph_backwards(output_tensor.grad_fn, None)
+        # Find inputs
+        self.inputs = []
+        for v in self.tensor_map.values():
+            # Is an input if the parents dict is empty.
+            if bool(v.parents):
+                self.inputs.append(v)
+
+    def build_graph_backwards(self, fn, previous_fn):
+        id = get_unique_id_for_fn(fn)
+        if id in self.tensor_map:
+            node = self.tensor_map[id]
+            node.add_child(previous_fn)
+        else:
+            node = GraphNode(fn)
+            self.tensor_map[id] = node
+            # Propagate to children
+            for child_fn in fn.next_functions:
+                node.add_parent(self.build_graph_backwards(child_fn, fn))
+        return node

codes/train.py

@@ -30,7 +30,7 @@ def init_dist(backend='nccl', **kwargs):
 def main():
     #### options
     parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_cifar_rrdb.yml')
+    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_imgset_rrdb_xl_wideres.yml')
     parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none',
                         help='job launcher')
     parser.add_argument('--local_rank', type=int, default=0)